Project Summary Sudden cardiac arrest (SCA) is an important healthcare issue, with ~350,000 events annually in the U.S. With our long-term goal to reduce the rate of SCA, we initiated a study of sudden cardiac death (SCD) mechanisms in the community. We used a widely available, inexpensive, and non-invasive tool (resting 12-lead ECG) to characterize the electrophysiological substrate of SCD. We further incorporated novel signal processing and vectorcardiographic approaches to represent mechanistic electrophysiological phenomena. In conjunction with genome-wide association studies (GWAS) of these mechanistic ECG phenotypes, we are aiming to discover underlying biology and novel mechanisms of SCD. We also developed a SCD risk stratification tool. During the first funding period, we analyzed data from the Atherosclerosis Risk in Communities (ARIC) and Cardiovascular Health Study (CHS) cohorts. In this application, we build on our prior work by continuing further development of mechanistic ECG phenotypes and also adding additional diverse cohorts. We will focus our studies on the U-loop and T-loop ECG phenotypes, which represent distinct mechanistic electrophysiological phenomena, and do not correlate with existing components of our risk score. To adequately consider sex as a biological variable, to conduct analyses by racial / ethnic subgroups, to validate the SCD risk score and increase power for GWAS, we are adding three large diverse NIH-funded cohorts: the Jackson Heart Study (JHS), the Women's Health Initiative (WHI), and the Multi-Ethnic Study of Atherosclerosis (MESA). We hypothesize that mechanistic ECG markers of the electrophysiological substrate, derived from the resting 12-lead ECG (1) are associated with SCD; (2) improve SCD risk prediction in comparison to the previously developed clinical-only and GEH-SCD risk scores, and (3) are independently associated with genetic factors. Also, we hypothesize that (4) the pattern of longitudinal changes in ECG phenotypes carries additional predictive value, and (5) changes in the electrophysiological substrate cause mechanical dyssynchrony and left ventricular dysfunction. We will use Mendelian randomization to determine whether the observed association between the studied ECG phenotypes and SCA and between ECG phenotype and known SCA substrates (coronary heart disease, heart failure, atrial fibrillation, ventricular conduction abnormalities) is causal.